Notifications can deliver significant engagement uplift, but, if abused, may result in negative user sentiment. What we routinely see at Phiture, is that teams are far more likely to under-deliver on notifications impact due to fear of annoying users than they are to drive users away through over-communicating.

All too often, in an effort to be ‘data driven’, teams fall into one or more of the following traps:
- Tracking the wrong things
- Tracking everything possible
- Failing to effectively structure and process
- Analysis paralysis
- Failing to keep analytics up to date

Mobile attribution is one of the cornerstones for growth-oriented apps and part of the fundamental tech layer of the Mobile Growth Stack. According to Mobbo, 80% of the Top 500 apps on iOS, have implemented an attribution SDK.

With more than 20,000 games released in the App Store per month, getting discovered is tough for new mobile games. But what if players knew about the game in advance and actively anticipated its launch as fans?

Paid User Acquisition (UA) is the most important scalable growth channel for apps, and the one that app developers have most control over— leveraging audiences, ad networks, budgets, and ad creatives. Yet it can be daunting to spend thousands on acquiring new users without knowing a certain outcome.

Tech choices will seldom be the most impactful decisions when it comes to driving growth, but poor tech choices or improper configuration of tools will definitely inhibit what a growth team can do, instead sowing confusion and frustration within the organization.

In this article, I outline a framework that helps prioritize development of programmatically driven notifications (applicable to mobile and web push notifications, email, SMS and potentially channels).

One tough challenge, especially with small teams, is deciding how much emphasis to allocate to product (e.g. new features, functionality or usability improvements) vs growth (sustainably growing the active user base), especially when it comes to engineering resources, which are usually the scarcest and most expensive to deploy.